Non-Probability sampling

 Basic Terminologies
 Population
 Sample and Sampling
 Advantages & Disadvantages of
Sampling
 Non-Probability Sampling

 Population or Universe: The group of
people, items or units under investigation
and includes every individual.
 Sample: A collection consisting of a part or
SUBSET of the objects or individuals of population
which is selected for the purpose, representing the
population
 Sampling: It is the process of selecting a sample
from the population. For this population is divided
into a number of parts called Sampling Units.

 Large population can be conveniently
covered.
 Time, money and energy is saved.
 Helpful when units of area are
homogenous.
 Used when percent accuracy is not
acquired.
 Used when the data is unlimited.

 Economical: Reduce the cost compare to entire
population.
 Increased speed: Collection of data, analysis and
Interpretation of data etc take less time than the
population.
 Accuracy: Due to limited area of coverage,
completeness and accuracy is possible.
 Rapport: Better rapport is established with the
respondents, which helps in validity and reliability of
the results

 Biasedness: Chances of biased selection leading
to incorrect conclusion
 Selection of true representative sample:
Sometimes it is difficult to select the right
representative sample
 Need for specialized knowledge: The researcher
needs knowledge, training and experience in
sampling technique, statistical analysis and
calculation of probable error
 Impossibility of sampling: Sometimes population is
too small or too heterogeneous to select a
representative sample.

 A true representative of the population.
 Free from error due to bias.
 Adequate in size for being reliable.
 Units of sample should be independent and
relevant
 Units of sample should be complete precise
and up to date
 Free from random sampling error
 Avoiding substituting the original sample for
convenience.

1. Probability Sampling: A probability sample
is one in which each member of the
population has an equal chance of being
selected.
2. Non-Probability Sampling: Nonprobability
Sample a particular member of the
population being chosen is unknown.
 In probability sampling, randomness is the
element of control. In Non-probability
sampling, it relies on personal judgment.

1. Purposive Sampling: Researcher selects a
"typical group" of individuals who might
represent the larger population and then collects
data from this group. Also known as Judgmental
Sampling.

2. Convenience Sampling : Obtaining units or
members who are most conveniently
available. It consists of units which are
obtained because cases are readily available.
Researcher determines the required sample size
and then simply collects data on that number of
individuals who are available easily.

3. Quota Sampling: The selection of the sample is
made by the researcher, who decides the quotas
for selecting sample from specified sub groups of
the population.
 For example, an interviewer might be needed data
from 40 adults and 20 adolescents in order to study
students’ television viewing habits.
Selection will be
 20 Adult men and 20 adult women
 10 adolescent girls and 10 adolescent boys

4. Snowball Sampling:
 In snowball sampling, the researcher
Identifying and selecting available
respondents who meet the criteria for
inclusion.
 After the data have been collected from the
subject, the researcher asks for a referral of
other individuals, who would also meet the
criteria and represent the population of
concern.
 chain sampling, chain-referral, sampling
referral sampling

Non-Probability sampling

Non-Probability sampling

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